A Novel Low-Dimensional Sparse and Low-Rank Representation Method for Single-Cell RNA Sequencing Data Clustering

The advancement of single-cell RNA sequencing (scRNA-seq) technology has enabled researchers to capture cellular heterogeneity at the individual cell level, driving progress in diverse fields such as developmental biology, immunology, and cancer research. Accurate cell clustering is a crucial step for researchers utilizing scRNA-seq data; however, inherent characteristics like high dimensionality and sparsity pose significant challenges to obtaining precise clustering results. To achieve accurate clustering, this paper proposes a novel approach that integrates dimensionality reduction, self-representation matrix construction, and the clustering process into an end-to-end model termed LDSLRR (Low-Dimensional Sparse and Low-Rank Representation). Specifically, the original gene expression matrix first undergoes dimensionality reduction via projection. Subsequently, low-rank representation combined with a sparsity constraint facilitates the learning of the self-representation matrix. Finally, the cluster assignment matrix is acquired using graph-regularized non-negative matrix factorization (NMF). These three modules are simultaneously optimized, enhancing the accuracy of the clustering results. Comparative experiments against multiple state-of-the-art clustering methods on various scRNA-seq datasets demonstrate the superiority of the proposed LDSLRR method.